Satellite automatic identification system (ais) for determining actual spoofing maritime vessels and associated geographic spoof sizes and related methods

ABSTRACT

A satellite Automatic Identification System (AIS) is for tracking a plurality of maritime vessels and may include a ground AIS server and a constellation of Low-Earth Orbit (LEO) satellites in communication with the ground AIS server. Each LEO satellite may include an AIS payload configured to receive AIS messages from the plurality of maritime vessels and determine therefrom reported vessel position data, determine actual signal arrival measurements for the AIS messages, and determine a potential spoofing maritime vessel based upon the reported vessel position data and actual signal arrival measurements. The ground AIS server may be configured to determine an actual spoofing maritime vessel and associated geographic spoof size based upon the reported vessel position data and actual signal arrival measurements for the potential spoofing maritime vessel.

TECHNICAL FIELD

The present invention relates to the field of identification systems,and, more particularly, to a satellite-based Automatic IdentificationSystem (AIS) for tracking a plurality of maritime vessels and relatedmethods.

BACKGROUND

While AIS was originally employed to provide collision avoidance formaritime vessels, its application has been extended into maritimesurveillance using terrestrial and space-borne receivers. The AISsystem, however, is a self-monitored reporting system and misreportingoccurs from equipment failures or deliberately by “bad actors.” These“bad actors” may use a number of techniques to “spoof” the reported AISdata to avoid detection from authorities, which may endanger othermarine vessels by not correctly reporting their position. Further,localized global positioning system (GPS) jamming could result insignificant misreporting because AIS normally uses a vessel's GPS toreport its position, and thus, reporting incorrect GPS coordinates bythe AIS endangers all maritime vessels in a jammed area.

In addition, there are any number of vessels that may be inadvertentlyor deliberately misreporting. For example, vessels may be “Zero MMSI”,meaning that they do not have a registered device. Alternatively,vessels may report the same Maritime Mobile Service Identity (MMSI) asother vessels, which may occur when the MMSI is “pirated” or possiblyobtained from used equipment. This is often a result of MMSI assignmentto a device being permanent or difficult to change as required by theFCC. Also, some vessels may lack GPS information reporting capabilitybecause of a broken GPS, GPS interface, or the GPS was disconnected, forexample. Some vessels may configure their AIS to “flip” the sign oflatitude and/or longitude data when reporting to avoid detection. Theremay be (or soon may be) more refined methods to manipulate the AISreporting data such as offsetting or walking location reporting, forexample.

Various approaches have been developed for validating AIS reportingdata. One such approach is set forth in U.S. Patent Application No.2018/0123680 to Stolte et al. As the AIS data is received, the data istagged with a measured frequency offset from a nominal frequency and atime delay to determine actual signal propagation delay. An expectedfrequency and time offset based upon a position report and latestsatellite ephemeris are then calculated to determine an expected signalpropagation delay. If a comparison of the measured signal parameters(e.g., propagation delay) to the expected signal parameters (e.g.,propagation delay) exceeds a threshold, then the AIS data is flagged assuspect.

Despite the advantages provided by such systems, further improvementsmay be desirable for more efficient and accurate validation of AIS data.

SUMMARY

A satellite Automatic Identification System (AIS) is for tracking aplurality of maritime vessels and may include a ground AIS server and aconstellation of Low-Earth Orbit (LEO) satellites in communication withthe ground AIS server. Each LEO satellite may include an AIS payloadconfigured to receive AIS messages from the plurality of maritimevessels and determine therefrom reported vessel position data, determineactual signal arrival measurements for the AIS messages, and determine apotential spoofing maritime vessel based upon the reported vesselposition data and actual signal arrival measurements. The ground AISserver may be configured to determine an actual spoofing maritime vesseland associated geographic spoof size based upon the reported vesselposition data and actual signal arrival measurements for the potentialspoofing maritime vessel.

In an example embodiment, the ground AIS server may be furtherconfigured to generate a credibility score for the actual spoofingmaritime vessel based upon a history of associated geographic spoofsizes. By way of example, the ground AIS server may be configured togenerate the credibility score based upon different spoofing thresholdscorresponding to different geographical locations. In one exampleimplementation, the ground AIS server may be configured to increment ordecrement the credibility score over time based upon the history ofassociated geographic spoof sizes. The ground AIS server may beconfigured to generate the credibility score based upon Bayesianevidence accrual, for example. In some embodiments, the ground AISserver may be configured to send an alert to a remote site based uponthe credibility score. In another example implementation, the ground AISserver may generate the credibility score further based upon at leastone of a port visited, a port visit time, and a port visit duration.

By way of example, the actual signal arrival measurements may compriseactual frequency of arrival (FOA) measurements or actual time of arrival(TOA) measurements. The satellite AIS of claim 1 wherein the AIS payloaddetermines potential spoofing maritime vessels based upon a differencebetween a corresponding expected signal arrival measurement and theactual signal arrival measurement for the AIS messages.

A related ground AIS server may include a receiver, a memory, and aprocessor configured to cooperate with the receiver and the memory toreceive AIS messages from the plurality of maritime vessels via aconstellation of Low-Earth Orbit (LEO) satellites, the AIS messagesincluding reported vessel position data, and receive actual signalarrival measurements for AIS messages from the LEO satellites for apotential spoofing maritime vessel. The processor may further determinewhether the potential spoofing maritime vessel is an actual spoofingmaritime vessel and associated geographic spoof size based upon thereported vessel position data and actual signal arrival measurements forthe potential spoofing maritime vessel.

A related method is for tracking a plurality of maritime vessels usingan AIS system, such as the one described briefly above. The method mayinclude, at each AIS payload, receiving AIS messages from the pluralityof maritime vessels and determine therefrom reported vessel positiondata, determining actual signal arrival measurements for the AISmessages, and determining a potential spoofing maritime vessel basedupon the reported vessel position data and actual signal arrivalmeasurements. The method may further include determining, at the groundAIS server, an actual spoofing maritime vessel and associated geographicspoof size based upon the reported vessel position data and actualsignal arrival measurements for the potential spoofing maritime vessel.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an exemplary satellite AutomaticIdentification System (AIS) for tracking a plurality of maritime vesselsand performing spoof detection in accordance with an example embodiment.

FIG. 2 is a schematic block diagram illustrating an exemplary embodimentof the satellite AIS system of FIG. 1 providing spoof detection basedupon frequency of arrival (FOA) data.

FIG. 3 is schematic block diagram of an example AIS payload which may beused with the system of FIG. 2 .

FIG. 4 is plot of predicted vs. actual Doppler profile data points in anexample spoof detection scenario using the system of FIG. 2 .

FIG. 5 is a flow diagram illustrating method aspects associated with thesystem of FIG. 2 .

FIG. 6 is a schematic block diagram illustrating another exemplaryembodiment of the satellite AIS system of FIG. 1 providing spoofdetection based upon time sequenced pairwise differential calculations.

FIG. 7 is a flow diagram illustrating method aspects associated with thesystem of FIG. 6 .

FIG. 8 is a schematic block diagram illustrating another exemplaryembodiment of the satellite AIS system of FIG. 1 providing spoofingmarine vessel detection and associated geographic spoof sizes.

FIG. 9 is a schematic block diagram illustrating an example dataprocessing algorithm for performing spoofing marine vessel detectionwithin the system of FIG. 8 .

FIG. 10 is a schematic block diagram illustrating an example spoofingmarine vessel detection scenario which may be implemented within thesystem of FIG. 8 using different vessel categories and geographicspoofing thresholds.

FIG. 11 is a flow diagram illustrating method aspects associated withthe system of FIG. 8 .

DETAILED DESCRIPTION

The present description is made with reference to the accompanyingdrawings, in which exemplary embodiments are shown. However, manydifferent embodiments may be used, and thus the description should notbe construed as limited to the particular embodiments set forth herein.Rather, these embodiments are provided so that this disclosure will bethorough and complete. Like numbers refer to like elements throughout,and prime and multiple prime notation are used to indicate similarelements in different embodiments.

Referring initially to FIGS. 1-3 , a satellite-based AutomaticIdentification System (AIS) 100 for tracking a plurality of maritimevessels 104 is first described. In particular, the system 100illustratively includes a constellation of Low-Earth Orbit (LEO)satellites 102. For example, the LEO satellite constellation may be theIridium NEXT constellation. Each LEO satellite 102 includes an AISpayload 108 for receiving AIS messages from the maritime vessels 104 andfor determining therefrom reported vessel position data andsatellite-based observation data. Maritime vessel speeds and headingsmay also be used.

The system 100 also illustratively includes a ground AIS server 210configured to obtain the reported maritime vessel position data andsatellite-based observation data from the LEO satellites 102 over time.The ground AIS server 210 includes a processor and a memory associatedtherewith including non-transitory computer-executable instructions forperforming the various operations discussed further below. In practice,there may be multiple ground AIS servers 210 which may be geographicallydistributed (e.g., in a cloud configuration), although a single groundAIS server is shown in FIG. 2 for clarity of illustration.

In operation, the maritime vessels 104 transmit AIS messages in whichthe vessel encodes its respective information as part of a 256-bitstring of data, which includes sections or parts such as start and endflags, cyclic redundancy check (CRC) data, and a 168-bit data sectionfor the vessel data. The vessel data typically includes the identity ofthe vessel, position data, heading, speed, and other pertinentinformation (i.e., AIS message content), and the AIS message isbroadcast from the vessel and received by one or more LEO satellites 102orbiting the earth.

The transmissions 202 are decoded by the LEO satellite(s) 102 thatreceives the transmissions 202. In turn, this decoded information andsatellite-based observation data is sent by transmissions 204 to theground antennas 106. The ground antennas 106 are in communication withthe ground AIS server 210 (e.g., via a wide area network or Internet),which processes both the vessel data and satellite-based observationdata to validate reported positions of the respective maritime vessels104 to generate position validation (PV) data. The PV data may beaccessed by user computers 216 via a cloud 214 or Internet, for example.

Each AIS payload 108 illustratively includes a receiver 110, transmitter112, and processor 114 which are configured to receive AIS messages fromthe maritime vessels 104 and determine therefrom reported vesselposition data from the AIS message content. Upon receiving AIS messages,the processor 114 determines an actual frequency of arrival (FOA) foreach of the AIS messages, as well as an expected FOA for each of the AISmessages based upon the reported vessel position data for each AISmessage. The frequency offset is affected by the closing rate of thesatellite 102 to the vessel 104 on the earth. Moreover, the position(altitude and latitude/longitude) and speed/heading of each LEOsatellite 102 is known with a relatively high degree of precision,allowing the AIS payload 108 to not only accurately measure the actualFOA, but also to estimate what the expected FOA should have been for avessel 104 at the position reported in the AIS message. If a differencebetween a corresponding expected FOA and actual FOA for a given AISmessage exceeds a threshold, the processor 114 determines the givenvessel 104 to be a potential spoofing maritime vessel, and sends apotential spoof alert to the ground AIS server 210 accordingly. That is,a significant difference from reported position and measured characterindicates faulty equipment or a “bad actor” (i.e., measurements do notsupport reported position).

Performing the potential spoof detection at the AIS payloads 108provides a significant technical advantage. In particular, transmissionbandwidth from the satellites 102 to ground may be relatively expensive,and sending extra data beyond the AIS messages themselves, such assignal arrival measurements, can be costly. Moreover, to transmit thisextra data for all AIS messages would consume additional data storageand processing resources. Yet, in the present example, the AIS payloads108 determine potential spoofing marine vessels 104 as AIS messages arereceived, and may accordingly send only the signal measurement dataassociated with messages from the spoofing marine vessels to therebysave transmission bandwidth, as well as storage and processingresources. Moreover, this approach allows for a potential spoof to bedetermined with as little as one measurement at the AIS payload 108,rather than having to process data over a series of occurrences to makesuch a determination. However, in some embodiments, more than onemeasurement may be used in the determination, as well as other factors.

For example, each AIS payload 108 may be further configured to determinean actual time of arrival (TOA) for each of the received AIS messages,and determine an expected TOA for each of the AIS messages based uponthe reported vessel position data for the AIS message. Thus, theprocessor 114 may determine the potential spoofing maritime vesselfurther based upon a difference between a corresponding expected TOA andactual TOA for the given AIS message, as similarly described above withreference to the FOA difference. That is, the processor 114 may look toboth the FOA and TOA differences, and determine a potential spoofingmaritime vessel 104 when both of the differences exceed a respectivefrequency or time threshold. In some embodiments, a TOA difference maybe used without the FOA difference to determine potential spoofing, aswill be discussed further below.

As noted above, the AIS payload 108 sends AIS messages and theassociated expected FOAs and actual FOAs to the ground AIS server 210for the potential spoofing maritime vessel. The ground AIS server 210 isconfigured to determine an estimated position of the potential spoofingmaritime vessel based upon the plurality of AIS messages and associatedexpected FOAs and actual FOAs. One example approach is illustrated inthe plot 140 of FIG. 4 . The ground AIS server 210 compares a sequenceof AIS messages (per MMSI) reporting GPS position against thecorresponding sequence of physical attributes (e.g., FOA) measured onthe received signal by the AIS payload(s) 108, as shown. To determinethe difference between reported and true position, a sequence ofmeasurements may be used leveraging non-linear least squares processing.In the illustrated example, points 141 correspond to actual Dopplerprofile measurements for received AIS messages, whereas points 142correspond to the expected Doppler profile measurement based upon thereported position. Over a series of data points, a predicted Dopplerprofile may be determined corresponding to the actual path of thespoofing vessel 104.

Generally speaking, precision improves with increased observations overa collection arc, although uncorrected receiver biases can impactaccuracy regardless of data volume collected. The smaller the covariancethe more precise the estimated position is, and thus smaller the spoofsthat can be detected. For this approach, it may be assumed that thetarget emitter and receiver 110 biases are calibrated out or negligibleto provide accurate geolocations, for example. The ground AIS server 210may also leverage satellite ephemeris data (known data that representsthe trajectory of the satellite over time) when determining theestimated position of the potential spoofing maritime vessel 104.Various third party ephemeris data may be used. The least squaresapproaches requires neither assumptions on errors nor complicatedcomputations typical of other approaches.

In some embodiments, different threshold error boundaries may be usedfor determining what is a spoof. For example, different thresholds maybe used for different MMSI's, such as based upon class of ship, etc. So,for example, cruise ships might have a relatively large (e.g., 20 km)boundary, whereas oil ships might have a much smaller threshold boundary(e.g., 1 km) to determine whether a spoof is occurring. Moreover, theAIS payload 108 may also continue to examine data for a potentialspoofing vessel 104 until the vessel comes back within its respectivethreshold boundary, at which point it may discontinue sending the fulldata package including signal measurement data to the ground AIS server210. This advantageously helps save signal bandwidth and processingresources, as discussed above.

A related method for tracking maritime vessels 104 using the satelliteAIS 100 is now described with reference to the flow diagram 150 of FIG.5 . Beginning at Block 151, the method illustratively includes, at eachAIS payload, receiving AIS messages from the maritime vessels 104 anddetermining therefrom reported vessel position data (Block 152),determining an actual FOA for each of the AIS messages (Block 153),determining an expected FOA for each of the AIS messages based upon thereported vessel position data for each AIS message (Block 154),determining a potential spoofing maritime vessel based upon a differencebetween a corresponding expected FOA and actual FOA for a given AISmessage, and sending a potential spoof alert to the ground AIS server210 (Blocks 155-156). The method further illustratively includesreceiving the potential spoof alert at the ground AIS server 210, and insome embodiments the expected/actual FOA data, and determining estimatedpositions of the spoofing maritime vessels 104, at Block 157, asdiscussed further above. The method of FIG. 5 illustratively concludesat Block 158.

Turning now to FIG. 6 , another example AIS system 100′ is described inwhich the ground AIS server 210′ may be configured to perform timesequenced pairwise differential calculations of actual signal arrivalmeasurements (e.g., differential FOA (DFOA) and/or TOA (DFOA)) from oneor more AIS payloads for a potential spoofing maritime vessel toestimate a position of the potential spoofing maritime vessel. Thisadvantageously allows for mitigation of emitter/receiver bias to therebyprovide more accurate position estimates of spoofing maritime vessels.

By way of background, emitter/signal bias can be a significant issuewhen attempting to measure time or frequency of arrival of AIS signals.Aid to navigation (A2N) sites support AIS signals and can be used as asource for receiver calibration. However, A2Ns are generally not wellcontrolled for either frequency emitted or time of transmission.Moreover, A2Ns will only provide calibration for AIS receivers, but thisdoes nothing for biases of vessel 104′ emitters. This is problematic inthat the standard on AIS transmitters allows for up to a 450 Hz error,which would result in a significant “Doppler” offset (and, thus, errorin estimated position). Moreover, orbital average power (OAP)limitations for a payload may require power-cycling of an AIS payload,and hence there may be payload drift due to power cycles and operationalconditions. Further, drift impacting the accuracy of payloadmeasurements between calibration cycles may still cause errors even for“always on” payloads. Thus the pairwise differential approach advantagesare appreciated by those skilled in the art.

The ground AIS server 210′ may use time of arrival and/or frequency ofarrival estimates from a given AIS payload without having to calibratefor AIS emitter or receiver bias. More particularly, the use of timesequenced pairwise measurements from a single AIS receiver platformremoves target emitter and AIS receiver biases, and provides a basis formore accurate geolocation.

In the relationships below the subscripts x, y, and z generically relateto the typical Cartesian position coordinates. The subscript 1generically indicates a “label” for the position of a vessel. Thesubscript “s” generically indicates the position of the satellite AISobserver. There are a total of N measurements available, and the integertime index represented the time ordering of the observables. In generalboldface quantities indicate a vector, and this allows more compactnotation by suppressing individual x,y,z components for example.

With respect to differential time of arrival(DTOA), given a signaltransmitted at some known time and the measurement includes a biasτ_(b), the signal time of arrival at the observation n is:

${{\tau\lbrack n\rbrack} = {{{\frac{1}{c}\sqrt{\left( {{p_{1x}\lbrack n\rbrack} - p_{sx}} \right)^{2} + \left( {{p_{1y}\lbrack n\rbrack} - p_{sy}} \right)^{2} + \left( {{p_{1z}\lbrack n\rbrack} - p_{sz}} \right)^{2}}} + \tau_{b}} = {{\frac{1}{c}{❘{{p_{1}\lbrack n\rbrack} - p_{s}}❘}} + \tau_{b}}}},{n = {1\ldots N}}$

Assuming the time bias Tb of the system (Tx+Rx) is constant over acollection interval, the computed time differences between measurementsto cancel this bias is given by:

${{{DTOA}:\Delta{\tau\lbrack n\rbrack}} = {{{\tau\left\lbrack {n + 1} \right\rbrack} - {\tau\lbrack n\rbrack}} = {{\frac{1}{c}{❘{{p_{1s}\left\lbrack {n + 1} \right\rbrack} - p_{s}}❘}} - {\frac{1}{c}{❘{{p_{1s}\lbrack n\rbrack} - p_{s}}❘}}}}},{n = {{1\ldots N} - 1}}$

With respect to differential frequency of arrival (DFOA), a signal istransmitted from a source at a nominal frequency of v₀ plus some unknownoffset v_(s) (here the subscript “s” indicates the terrestrial AISsource) and then received and down-converted by a receiver having anunknown frequency offset of v₁. On observation n, the received,down-converted signal will have a frequency of:

${{v_{1s}\lbrack n\rbrack} = {{\left( {v_{0} + v_{s}} \right)\frac{1 - \frac{u_{1s}\left\lfloor n \right\rfloor}{c}}{\sqrt{1 - \left( \frac{u_{1s}\lbrack n\rbrack}{c} \right)^{2}}}} - \left( {v_{0} + v_{1}} \right)}},{n = {1\ldots N}}$

where the relative velocity u_(ls)[n] between a stationary source s anda moving receiver can be computed as

${{u_{1s}\lbrack n\rbrack} = {{{v_{1}\lbrack n\rbrack} \cdot \frac{{p_{1}\lbrack n\rbrack} - p_{s}}{❘{{p_{1}\lbrack n\rbrack} - p_{s}}❘}} = {{v_{1}\lbrack n\rbrack} \cdot \frac{{p_{1}\lbrack n\rbrack} - p_{s}}{\sqrt{\left( {{p_{1x}\lbrack n\rbrack} - p_{sz}} \right)^{2} + \left( {{p_{1y}\lbrack n\rbrack} - p_{sy}} \right)^{2} + \left( {{p_{1z}\lbrack n\rbrack} - p_{sz}} \right)^{2}}}}}},{n = {1\ldots N}}$

and positive values indicate that the platform is receding from thesource. Assuming that u_(ls)<<c, we can then approximate the frequencyof arrival as

${{v_{1s}^{\prime}\lbrack n\rbrack} = {{{- v_{0}}\frac{u_{1s}\lbrack n\rbrack}{c}} + v_{b}}},{n = {1\ldots N}}$

Here, the source and receiver frequency offsets have been combined intoa single constant bias term vb. Assuming this bias term remains constantover the collection interval, the normalized frequency differences ofarrival between measurements may be calculated as

${{{DFOA}:\Delta{v\lbrack n\rbrack}} = {\frac{{v_{1s}^{\prime}\left\lbrack {n + 1} \right\rbrack} - {v_{1s}^{\prime}\lbrack n\rbrack}}{v_{0}} = {{{- \frac{1}{c}}{{v_{1}\left\lbrack {n + 1} \right\rbrack} \cdot \frac{{p_{1}\left\lbrack {n + 1} \right\rbrack} - p_{s}}{❘{{p_{1}\left\lbrack {n + 1} \right\rbrack} - p_{s}}❘}}} + {\frac{1}{c}{{v_{1}\lbrack n\rbrack} \cdot \frac{{p_{1}\lbrack n\rbrack} - p_{s}}{❘{{p_{1}\lbrack n\rbrack} - p_{s}}❘}}}}}},{n = {{1\ldots N} - 1}}$

Using the above DTOA and DFOA results provides the observable modelfunction as

${f\left( {\theta,z} \right)} = \begin{bmatrix}{\Delta\tau} \\{\Delta\upsilon}\end{bmatrix}$

where θ is a vector of the parameters to estimate (the sourcegeolocation), and z is a vector of known system parameters (e.g.,platform ephemerides). Expanding this to a full vector of measurementsgives

${f\left( {\theta,z} \right)}\begin{bmatrix}{\Delta{\tau\lbrack 1\rbrack}} \\ \vdots \\{\Delta{\tau\left\lbrack {N - 1} \right\rbrack}} \\{\Delta{v\lbrack 1\rbrack}} \\ \vdots \\{\Delta{v\left\lbrack {N - 1} \right\rbrack}}\end{bmatrix}$

and a vector of parameters to estimate and system parameters:

$\theta = {{\begin{bmatrix}p_{sx} \\p_{sy}\end{bmatrix}{and}z} = \begin{bmatrix}{p_{1}\lbrack 1\rbrack} \\{p_{1}\lbrack N\rbrack} \\ \vdots \\{v_{1}\lbrack 1\rbrack} \\{v_{1}\lbrack N\rbrack}\end{bmatrix}}$

Where H is the 2(N−1)×2 Jacobian matrix:

$H = {\frac{\partial{f\left( {\theta,z} \right)}}{\partial\theta} = \begin{bmatrix}\frac{{\partial\Delta}{\tau\lbrack 1\rbrack}}{\partial p_{sx}} & \frac{{\partial\Delta}{\tau\lbrack 1\rbrack}}{\partial p_{sy}} \\\frac{\partial{{\Delta\tau}\left\lbrack {N - 1} \right\rbrack}}{\partial p_{sx}} & \frac{{\partial\Delta}{\tau\left\lbrack {N - 1} \right\rbrack}}{\partial p_{sx}} \\\frac{{\partial\Delta}{v\lbrack 1\rbrack}}{\partial p_{sx}} & \frac{{\partial\Delta}{v\lbrack 1\rbrack}}{\partial p_{sy}} \\ \vdots & \vdots \\\frac{{\partial\Delta}{v\left\lbrack {N - 1} \right\rbrack}}{\partial p_{sx}} & \frac{{\partial\Delta}{v\left\lbrack {N - 1} \right\rbrack}}{\partial p_{sx}}\end{bmatrix}}$

Finding an estimate of θ by minimizing square error for a nonlinearproblem can be stated mathematically as

e = f(θ̂, z) − f(θ, z) ${\min\limits_{\hat{\theta}}J} = {e^{T}e}$

Often this problem is solved iteratively. To initialize the iterativesolution, an initial guess for the emitter location is made using anyinformation available, and a ENU coordinate system for this iteration iscentered at this point. Next, the nonlinear measurement model islinearized (attaching ENU system at current iterate) as follows:

f(θ,z)=f({circumflex over (θ)}_(n) ,z)+H(θ−{circumflex over (θ)}_(n))

The iteration is defined to determine the “next guess”:

(H ^(T) H)⁻¹ H ^(T) [f(θ,z)−f({circumflex over (θ)}_(n) ,z)]+{circumflexover (θ)}_(n)={circumflex over (θ)}_(n+1)

The current ENU solution is then projected to a spheroid (e.g., WGS-84is a standard model maintained by the US government), and the ECEF(Earth Centered Earth Fixed) coordinates obtained. Finally, adetermination is made as to whether suitable convergence is obtained(i.e., sequential differences in iterates), otherwise for the nextiterate a new ENU system is defined at the current ECEF estimate, andthe process repeats. As will be appreciated by those skilled in the art,the ECEF coordinate system is globally defined with one origin at thecenter of the spheriod, and an ENU system is “locally” defined

The foregoing will be further understood with reference to an exampleuse case. As a satellite 102′ flies over a target vessel 104′, it willreceive a sequence of AIS measurements during the time window that thevessel is “visible” to the satellite (i.e., the satellite goes beyondthe horizon of the vessel). For this example, it will be assumed thatthe satellite 102′ receives six AIS messages from the target vessel 104′during the time window. In the case of TOA, subtracting the first TOAfrom the second TOA, etc., will result in five DTOAs. Assuming that theemitter bias is the same across all of the different transmissions, theemitted bias will accordingly be cancelled out across these differences.From a given satellite 102′ platform, you get a sequence of differentialmeasurements, and since the bias from the emitter is relatively constantyou can use one to cancel out the other. Significantly, this biascancellation can be performed from a single platform, whereas othererror correct approaches may require a plurality of different platformsand/or A2N calibration, but the present approach does not. Anotheradvantage of this approach is that, even if emitter bias is drifting, itcan still cancel out because you are taking the differential from onemeasurement to the next. Another significant advantage is that thisapproach allows for processing without having to compensate forcomplicated temperature offsets.

The above-described approach automatically removes long-term biases andshort-term drift from both non-cooperative emitters (AIS sources) andthe satellite-based receiver. Moreover, this approach handles receiverseven during warm-up/power-cycles, in that drift only needs to be slowlyvarying between consecutive AIS message times for it to work. Moreover,this also provides a useful approach for “always on” payloads as well asthere is always some level of thermal-induced drift. Furthermore, thisapproach works despite non-cooperative target emitters (i.e., known orcontrolled emitters are not required). The ground AIS server 210′ mayaccordingly provide accurate spoofer geolocation, i.e., a location of aspoofer maritime vessel 104′ can be precisely developed with arelatively small number of single-platform differential measurementsusing nonlinear least squares. This provides additional benefits in thata least squares approach does not require any error assumptions (e.g.,density functions characterizing an error such as normality). Moreover,it provides improved location precision and reduces error ellipse,enabling increasing discrimination versus spoof location.

Referring additionally to the flow diagram 170 of FIG. 7 , beginning atBlock 171 a related method may include, at each AIS payload 108′,receiving AIS messages from the maritime vessels 104′ and determiningtherefrom reported vessel position data (Block 172), determining actualsignal arrival measurements for the AIS messages (Block 173),determining a potential spoofing maritime vessel based upon the reportedvessel position data and actual signal arrival measurements (Block 174),and sending a potential spoof alert to the ground AIS server 210′accordingly (Block 175). The method further illustratively includes, atthe ground AIS server 210′, performing time sequenced pairwisedifferential calculations of the actual signal arrival measurements froma given AIS payload and for the potential spoofing maritime vessel 104′at the ground AIS server for estimating a position of the potentialspoofing maritime vessel, at Block 176. The method of FIG. 7illustratively concludes at Block 177.

Turning now to FIGS. 8-10 , in accordance with another exampleimplementation of the AIS system 100″, the ground AIS server 210″ mayadvantageously determine actual spoofing maritime vessels along withassociated geographic spoof sizes based upon reported vessel positiondata from the AIS messages and actual signal arrival measurements (e.g.,FOA and/or TOA) for the potential spoofing maritime vessel 104″. Thegeographic spoof sizes may be factored into a “credibility” score thatis developed using various AIS-based information. This may be importantfor a number of applications, including early warning of approachingvessels with possible “bad intent” within a port or political boundary.

Various factors in addition to spoof size can be accumulated over timefor use in a vessel credibility score, such as: position spoof historyand size of spoofs; ports visited and time-of-day and duration of visit;vessel going “dark” and re-appearance; class of vessel; inconsistent AISmessages with respect to expected movements (e.g., location, class,etc.); travel in lanes for shipping; and duplicate MMSI or invalid MMSI.In an example implementation, Bayesian evidence accrual may be used togenerate the credibility scores. More particularly, Bayesian evidenceaccrual is an approach which allows for collecting a sequence ofobservations about the MMSI in question, as follows:

p(θ|x _(t);β)=K p(θ|x _(t) ,x _(t-1);β)=p(x _(t)|θ;β)p(θ|X _(t-1);β)

Where p(θ|X_(t);β) is the posterior distribution for all observed datathrough observation t; K is a superfluous constant;p(x_(t)|θ;β) is the likelihood of the observation x_(t) conditioned on θand β at t; p(θ|X_(t-1)|β) is a prior distribution over discretedecision choices for observation t; and β is a paramterization of knowndata, such as vessel type, flag, weather, hazards, etc. Each AISmeasurement may update the posterior evidence, which scores the entity's“credibility” (e.g., “Bad”, “Truthful”, or “Unknown”). A declaration ofthe “credibility” can be made using the posterior (e.g., maximuma-posterior rule). The geolocation is produced from received Doppler andtime of message receipt (TOMR), regardless if the MMSI is “truthful”,i.e., is a potential spoofer. This approach advantageously allows for“intelligent data pruning”, in that it avoids growing memory issues withthe storage of long histories, as will be discussed further below.

An example data collection scheme for Bayesian evidence accrual is shownin FIG. 9 . In addition to AIS measurements from the AIS payloads 108″,other data sources may include electro-optic (EO) and infrared (IR)sources 190″, radar sources 191″, signal intelligence (SIGINT) sources192″, and human intelligence (HUMINT) sources 193″. The ground AISserver 210″ may perform Bayesian evidence accrual from all of thesesources and utilize an inference rule 194″ (e.g., MAP, HPD, etc.) togenerate the respective credibility scores therefrom, for example.

The accumulation of evidence over time to revise credibility scores isdemonstrated in the example of FIG. 10 . Here, at a time 0 there isrelatively little data known about a vessel 104″ traveling along a path,which is represented in table (a). As subsequent data is acquired forthe vessel 104″ as it travels on the path to points 1, 2, and 3, thecredibility score changes. As noted above, different classes of vessels104″ may have different credibility thresholds associated with them.Here, cruise ships are generally considered to be among the mostreliable or truthful, followed by cargo ships and then speed boats. Notethat by the time a cruise ship completes the illustrated path, there isan extremely high confidence factor that it is engaged in lawfulactivity (98%), whereas for a speed boat traveling the same course theprobability of lawful activity is lower (60%) because of the class ofvessel, as well as other factors such as those discussed above (e.g.,inconsistent messages, going dark, etc.).

The above-described approach advantageously allows immediatevisualization of potentially “bad actors” and their true currentlocation. In some embodiments, a “trace back” may also be placed on anentity on a graphical user interface (GUI) to visualize its history. Theexploitation of various metrics (e.g., path deviation, port visits,etc.) using derived geolocation, as opposed to reported position, anddoing so world-wide provides for a more thorough and accuratedetermination of vessel 104″ credibility through the inclusion ofmulti-INT data, not just AIS data, to supplement the decision-makingprocess. The credibility score may be updated by various factors thatinfluence if a vessel might be nefarious—including where it has been,“lied about position”, “went dark”, etc.

Moreover, the Bayesian evidence accrual approach supports adaptivelearning where observations can be reprocessed at any time throughupdated likelihood functions. For example, likelihoods may be alteredwith new information, e.g., drug trafficking now being well-known for aport for vessels of a certain size or HUMINT or other data sources. Thepresent approach also allows prior data to be decision biased ornon-judgmental at the outset. The declaration/decision at eachobservation regarding credibility can be binary (Liar/Truthful) ormulti-level (e.g. 0-10), for example. Further, growing memory issues maybe avoided when using intelligent data pruning, keeping only the N mostsignificant likelihood contributors, and N need not be fixed across allvessels. It should also be noted that this approach may be used inconjunction with those discussed above, i.e., enhanced spoofingdetection at the AIS payloads and time sequenced pairwise differentialcalculations to provide still further memory and processing savings.

A related method for tracking maritime vessels 104″ is now describedwith reference to the flow diagram 200 of FIG. 11 . The method begins(Block 201) with, at each AIS payload, receiving AIS messages from themaritime vessels 104″ and determining therefrom reported vessel positiondata (Block 202), determining actual signal arrival measurements for theAIS messages (Block 203), determining a potential spoofing maritimevessel based upon the reported vessel position data and actual signalarrival measurements (Block 204), and sending the potential spoof alertsto the ground AIS server 210″ (Block 205), as discussed further above.The method further illustratively includes determining, at the groundAIS server 210″, an actual spoofing maritime vessel 104″ and associatedgeographic spoof size based upon the reported vessel position data andactual signal arrival measurements for the potential spoofing maritimevessel, at Block 206. The method of FIG. 11 illustratively concludes atBlock 207.

Many modifications and other embodiments will come to the mind of oneskilled in the art having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it isunderstood that the disclosure is not to be limited to the specificembodiments disclosed, and that modifications and embodiments areintended to be included within the scope of the appended claims.

1. A satellite Automatic Identification System (AIS) for tracking aplurality of maritime vessels, the AIS comprising: a ground AIS server;and a constellation of Low-Earth Orbit (LEO) satellites in communicationwith the ground AIS server, each LEO satellite comprising an AIS payloadconfigured to receive AIS messages from the plurality of maritimevessels and determine therefrom reported vessel position data, determineactual signal arrival measurements for the AIS messages, and determine apotential spoofing maritime vessel based upon the reported vesselposition data and actual signal arrival measurements; the ground AISserver configured to determine an actual spoofing maritime vessel andassociated geographic spoof size based upon the reported vessel positiondata and actual signal arrival measurements for the potential spoofingmaritime vessel.
 2. The satellite AIS of claim 1 wherein the ground AISserver is further configured to generate a credibility score for theactual spoofing maritime vessel based upon a history of associatedgeographic spoof sizes.
 3. The satellite AIS of claim 2 wherein theground AIS server is configured to generate the credibility score basedupon different spoofing thresholds corresponding to differentgeographical locations.
 4. The satellite AIS of claim 2 wherein theground AIS server is configured to increment or decrement thecredibility score over time based upon the history of associatedgeographic spoof sizes.
 5. The satellite AIS of claim 2 wherein theground AIS server is configured to generate the credibility score basedupon Bayesian evidence accrual.
 6. The satellite AIS of claim 2 whereinthe ground AIS server is configured to send an alert to a remote sitebased upon the credibility score.
 7. The satellite AIS of claim 2wherein the ground AIS server generates the credibility score based uponat least one of a port visited, a port visit time, and a port visitduration.
 8. The satellite AIS of claim 1 wherein the actual signalarrival measurements comprise actual frequency of arrival (FOA)measurements.
 9. The satellite AIS of claim 1 wherein the actual signalarrival measurements comprise actual time of arrival (TOA) measurements.10. The satellite AIS of claim 1 wherein the AIS payload determines thepotential spoofing maritime vessel based upon a difference between acorresponding expected signal arrival measurement and the actual signalarrival measurement for the AIS messages.
 11. A ground AutomaticIdentification System (AIS) server for tracking a plurality of maritimevessels, the ground AIS server comprising: a processor and associatedmemory configured to receive AIS messages from the plurality of maritimevessels via a constellation of Low-Earth Orbit (LEO) satellites, the AISmessages including reported vessel position data, receive actual signalarrival measurements for AIS messages from the LEO satellites for apotential spoofing maritime vessel, and determine whether the potentialspoofing maritime vessel is an actual spoofing maritime vessel andassociated geographic spoof size based upon the reported vessel positiondata and actual signal arrival measurements for the potential spoofingmaritime vessel.
 12. The ground AIS server of claim 11 wherein theprocessor is further configured to generate a credibility score for theactual spoofing maritime vessel based upon a history of associatedgeographic spoof sizes.
 13. The ground AIS server of claim 12 whereinthe processor is configured to generate the credibility score based upondifferent spoofing thresholds corresponding to different geographicallocations.
 14. The ground AIS server of claim 12 wherein the processoris configured to increment or decrement the credibility score over timebased upon the history of associated geographic spoof sizes.
 15. Theground AIS server of claim 12 wherein the processor is configured togenerate the credibility score based upon Bayesian evidence accrual. 16.The ground AIS server of claim 12 wherein the processor is furtherconfigured to send an alert to a remote site based upon the credibilityscore.
 17. A method for tracking a plurality of maritime vessels using asatellite Automatic Identification System (AIS) comprising a ground AISserver and a constellation of Low-Earth Orbit (LEO) satellites incommunication with the ground AIS server with each LEO satellitecomprising an AIS payload, the method comprising: at each AIS payload,receiving AIS messages from the plurality of maritime vessels anddetermine therefrom reported vessel position data, determining actualsignal arrival measurements for the AIS messages, and determining apotential spoofing maritime vessel based upon the reported vesselposition data and actual signal arrival measurements; and determining,at the ground AIS server, an actual spoofing maritime vessel andassociated geographic spoof size based upon the reported vessel positiondata and actual signal arrival measurements for the potential spoofingmaritime vessel.
 18. The method of claim 17 further comprising, at theground AIS server, generating a credibility score for the actualspoofing maritime vessel based upon a history of associated geographicspoof sizes.
 19. The method of claim 18 wherein generating comprisesgenerating the credibility score based upon different spoofingthresholds corresponding to different geographical locations.
 20. Themethod of claim 18 further comprising, at the ground AIS server,incrementing or decrementing the credibility score over time based uponthe history of associated geographic spoof sizes.
 21. The method ofclaim 18 wherein generating comprises generating the credibility scorebased upon Bayesian evidence accrual.
 22. The method of claim 18 furthercomprising, at the ground AIS server, sending an alert to a remote sitebased upon the credibility score.